A Non-Contact Computer Vision Based Analysis of Color in Foods

2007 ◽  
Vol 3 (5) ◽  
Author(s):  
Vural Gökmen ◽  
Idris Sügüt
Keyword(s):  
Computer Vision ◽  
Color Space ◽  
Digital Camera ◽  
Food Product ◽  
Color Measurement ◽  
Ann Model ◽  
Direct Transformation ◽  
B Values ◽  
Polygonal Area ◽  
Artificial Neural Network Ann

Since commercial colorimeters measure small area with a fixed geometry, the result of color measurement is usually unrepresentative for heterogeneous materials as in many food items. This paper describes a computer vision based approach for the measurement of color in a user defined polygonal area on the digital image of a food product. The algorithm used for color measurement converts the RGB values of the image captured by a digital camera to monitor L*a*b* values using the standard equations. The RGB responses for a captured image vary from one case to another, so, the direct transformation from RGB to L*a*b is not useful to obtain meaningful information about the color. Here, an artificial neural network (ANN) model was used to convert the monitor L*a*b* values into spectrophotometric L*a*b* values. The ANN model was calibrated by using the IT8 color chart consisting of 288 different colored squares which reflect all possible variations in the color space. The ?E values for the estimated values and the real spectrophotometric values were less than 0.45.

scholarly journals Application of Color and Size Measurement in Food Products Inspection

2019 ◽  
Vol 1 (2) ◽  
pp. 90 ◽  
Author(s):  
Joko Siswantoro
Keyword(s):  
Computer Vision ◽  
Color Space ◽  
Food Product ◽  
Color Measurement ◽  
Size Measurement ◽  
Specific Content ◽  
Color Changes ◽  
Length Width ◽  
Size Sorting ◽  
Mean Variance

Color and size are external aspects considered by consumers in purchasing a food product and are used in food product inspection using computer vision. This paper reviews recent applications of color and size measurement in food product inspection using computer vision. RGB, HSI, HSL, HSV, La*b spaces and color index are widely used to measure color in food product inspection. Color features, including value, mean, variance, and standard deviation of each channel in a color space are widely used in food product inspection. The applications of color measurement in food product inspection are for grading, detection of anomaly or damage, detection of specific content and evaluation of color changes. Length, width, thickness, average radius, Feret’s diameter, area, perimeter, volume, and surface area are common size measurements in food product inspection. The applications of size measurement in food product inspection are for estimating size, sorting, grading, detect unwanted objects or defects, and measurement of physical properties.

scholarly journals MnNH4P2O7-Based Coating for High Temperature Assessment on the Surfaces of Cement Composites

Coatings ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 396
Author(s):  
Rajagopalan Sam Rajadurai ◽  
Jong-Han Lee ◽  
Eunsoo Choi ◽  
Joo-Won Kang
Keyword(s):  
Room Temperature ◽  
Color Change ◽  
Heated Surface ◽  
Color Space ◽  
Digital Camera ◽  
B Values ◽  
Color Changes ◽  
Dark Violet ◽  
Increasing Temperature ◽  
Processing Techniques

This study examines the implementation of an MnNH4P2O7 (ammonium manganese III pyrophosphate)-based coating on structural elements to obtain temperature information with color changes. Based on the MnNH4P2O7 material, a coating was prepared and deposited on cement mortar surfaces. Heat experiments were then conducted to evaluate the thermochromism on the fabricated samples. The coated samples exhibited a superior irreversible thermochromic property at 400 °C with a color change from dark violet to light grayish blue at the heated surface. The color changes were retrieved at each temperature using a digital camera, and the change in color properties was evaluated in the RGB and L*a*b* color spaces using image processing techniques. With increasing temperature from room temperature, the RGB values were almost constant until 200 °C. At higher temperatures, the color changes started to accelerate until 400 °C. The values showed a 167%, 567%, and 49% increase in R, G, and B values, respectively, at 400 °C. In the L*a*b* color space, when the temperature was increased from room temperature to 400 °C, the L*a*b* values showed an increase of 211%, a decrease of 94%, and an increase of 78%, respectively.

scholarly journals Comparison between Color Spaces of Vita Lumin Shade Guide with Natural Teeth in Bengaluru Population using Spectrocolorimeter: An in vivo Study

2017 ◽  
Vol 18 (8) ◽  
pp. 683-687
Author(s):  
Deepak Mehta ◽  
Nishant Gupta ◽  
Rohit M Shetty ◽  
Adarsh N Bhat ◽  
Gopalakrishna Srivatsa ◽  
...  
Keyword(s):  
Local Population ◽  
Color Space ◽  
Color Measurement ◽  
Color Spaces ◽  
B Values ◽  
Natural Teeth ◽  
Shade Matching ◽  
Shade Guide ◽  
Shade Selection

ABSTRACT Aim The aim of this study is to compare the color space of Vita Lumin shade guide (SG) with the natural teeth of the local population. Materials and methods A total of 100 maxillary central incisors (100 patients) were subjected to color measurement with a spectrocolorimeter. For each tooth, L*, a*, b* values were recorded. All the shade tabs of Vita Lumin SG were analyzed with a spectrocolorimeter to define the color space covered by the Vita Lumin SG. The L*a*b* values of natural teeth were plotted on separate scattered diagrams and compared. Results About two out of three attributes (luminance and blue spectrum) of the local population of Bengaluru displayed a broader range than those available in Vita Lumin SG. Conclusion The local population requires an SG with an extended range, covering a higher luminance spectrum and broader blue spectrum. Clinical significance Esthetic restorations require accurate shade matching with the adjacent natural teeth, SGs being the mean of shade selection and communication should be comparable to the natural teeth. How to cite this article Shetty RM, Bhat AN, Gupta N, Mehta D, Srivatsa G, Singh I. Comparison between Color Spaces of Vita Lumin Shade Guide with Natural Teeth in Bengaluru Population using Spectrocolorimeter: An in vivo Study. J Contemp Dent Pract 2017;18(8):683-687.

scholarly journals Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6354
Author(s):  
Aimi Aznan ◽  
Claudia Gonzalez Viejo ◽  
Alexis Pang ◽  
Sigfredo Fuentes
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Imaging System ◽  
Cost Effective ◽  
Consumer Perception ◽  
Rice Grain ◽  
Ann Model ◽  
Practical Applications ◽  
Rice Samples ◽  
Artificial Neural Network Ann

Rice quality assessment is essential for meeting high-quality standards and consumer demands. However, challenges remain in developing cost-effective and rapid techniques to assess commercial rice grain quality traits. This paper presents the application of computer vision (CV) and machine learning (ML) to classify commercial rice samples based on dimensionless morphometric parameters and color parameters extracted using CV algorithms from digital images obtained from a smartphone camera. The artificial neural network (ANN) model was developed using nine morpho-colorimetric parameters to classify rice samples into 15 commercial rice types. Furthermore, the ANN models were deployed and evaluated on a different imaging system to simulate their practical applications under different conditions. Results showed that the best classification accuracy was obtained using the Bayesian Regularization (BR) algorithm of the ANN with ten hidden neurons at 91.6% (MSE = <0.01) and 88.5% (MSE = 0.01) for the training and testing stages, respectively, with an overall accuracy of 90.7% (Model 2). Deployment also showed high accuracy (93.9%) in the classification of the rice samples. The adoption by the industry of rapid, reliable, and accurate methods, such as those presented here, may allow the incorporation of different morpho-colorimetric traits in rice with consumer perception studies.

Questions to the Article: An Artificial Intelligence Model to Predict the Mortality of COVID-19 Patients at Hospital Admission Time Using Routine Blood Samples (Preprint)

2020 ◽  
Author(s):  
Shu-Chun Kuo ◽  
CHIEN WEI ◽  
Willy Chou
Keyword(s):  
Data Entry ◽  
Ann Model ◽  
Model Accuracy ◽  
Data Format ◽  
Raw Data ◽  
Probability Of Survival ◽  
Testing Data ◽  
Laboratory Results ◽  
Data Yield ◽  
Artificial Neural Network Ann

UNSTRUCTURED The recent article published on December 23 27 in 2020 is well-written and of interest, but remains several questions that are required for clarifications, including (1) 30 feature variables with normalized format(mean=0 and SD=1) required to compare model accuracy with those with the raw-data format; (2)inconsistency in variable numbers between entry and preview panels in Figure 4 and reference typos; and (3) data-entry format with raw blood laboratory results in Figure 4 inconsistent with the model designed using normalized data to estimate parameters. We conducted a study using the training and testing data provided by the previous study. An artificial neural network(ANN) model was performed to estimate parameters and compare the model accuracy with those eight models provided by the previous study. We found that (1) normalized data yield higher accuracy than that with the raw data; (2) typos definitely exist at the bottom review (=32>30 variables in the entry) panels in Figure 4 and typos in Table 6; and (3)the ANN earns a probability of survival(=0.91) higher than that(=0.71) in the previous study using the similar entry data when the raw data are assumed in the app. We also demonstrated an author-made app using the visualization to display the prediction result, which is novel and innovative to make the result improved with a dashboard in comparison with the previous study.

scholarly journals Predicting the 14-Day Hospital Readmission of Patients with Pneumonia Using Artificial Neural Networks (ANN)

2021 ◽  
Vol 18 (10) ◽  
pp. 5110
Author(s):  
Shu-Farn Tey ◽  
Chung-Feng Liu ◽  
Tsair-Wei Chien ◽  
Chin-Wei Hsu ◽  
Kun-Chen Chan ◽  
...  
Keyword(s):  
Neural Network ◽  
Early Stage ◽  
Characteristic Curve ◽  
Patient Discharge ◽  
Ann Model ◽  
Healthcare Settings ◽  
High Area ◽  
Icd 10 ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Unplanned patient readmission (UPRA) is frequent and costly in healthcare settings. No indicators during hospitalization have been suggested to clinicians as useful for identifying patients at high risk of UPRA. This study aimed to create a prediction model for the early detection of 14-day UPRA of patients with pneumonia. We downloaded the data of patients with pneumonia as the primary disease (e.g., ICD-10:J12*-J18*) at three hospitals in Taiwan from 2016 to 2018. A total of 21,892 cases (1208 (6%) for UPRA) were collected. Two models, namely, artificial neural network (ANN) and convolutional neural network (CNN), were compared using the training (n = 15,324; ≅70%) and test (n = 6568; ≅30%) sets to verify the model accuracy. An app was developed for the prediction and classification of UPRA. We observed that (i) the 17 feature variables extracted in this study yielded a high area under the receiver operating characteristic curve of 0.75 using the ANN model and that (ii) the ANN exhibited better AUC (0.73) than the CNN (0.50), and (iii) a ready and available app for predicting UHA was developed. The app could help clinicians predict UPRA of patients with pneumonia at an early stage and enable them to formulate preparedness plans near or after patient discharge from hospitalization.

scholarly journals Modeling Fused Filament Fabrication using Artificial Neural Networks

2021 ◽  
Author(s):  
Paul Oehlmann ◽  
Paul Osswald ◽  
Juan Camilo Blanco ◽  
Martin Friedrich ◽  
Dominik Rietzel ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Cost Effective ◽  
Print Quality ◽  
Ann Model ◽  
Production Environment ◽  
Fused Filament Fabrication ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

A comparative study and combined application of RSM and ANN in adsorptive removal of diuron using biomass ashes

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Sunil K. Deokar ◽  
Nachiket A. Gokhale ◽  
Sachin A. Mandavgane
Keyword(s):  
Three Dimensional ◽  
Current Approach ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Operational Parameters ◽  
Initial Concentration ◽  
Combined Application ◽  
Artificial Neural Network Ann ◽  
Relationship Of ◽  
Biomass Ashes

Abstract Biomass ashes like rice husk ash (RHA), bagasse fly ash (BFA), were used for aqueous phase removal of a pesticide, diuron. Response surface methodology (RSM) and artificial neural network (ANN) were successfully applied to estimate and optimize the conditions for the maximum diuron adsorption using biomass ashes. The effect of operational parameters such as initial concentration (10–30 mg/L); contact time (0.93–16.07 h) and adsorbent dosage (20–308 mg) on adsorption were studied using central composite design (CCD) matrix. Same design was also employed to gain a training set for ANN. The maximum diuron removal of 88.95 and 99.78% was obtained at initial concentration of 15 mg/L, time of 12 h, RHA dosage of 250 mg and at initial concentration of 14 mg/L, time of 13 h, BFA dosage of 60 mg respectively. Estimation of coefficient of determination (R 2) and mean errors obtained for ANN and RSM (R 2 RHA = 0.976, R 2 BFA = 0.943) proved ANN (R 2 RHA = 0.997, R 2 BFA = 0.982) fits better. By employing RSM coupled with ANN model, the qualitative and quantitative activity relationship of experimental data was visualized in three dimensional spaces. The current approach will be instrumental in providing quick preliminary estimations in process and product development.

scholarly journals User Automotive Powertrain-Type Choice Model and Analysis Using Neural Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 585
Author(s):  
Fabio Luis Marques dos Santos ◽  
Paolo Tecchio ◽  
Fulvio Ardente ◽  
Ferenc Pekár
Keyword(s):  
Internal Combustion Engine ◽  
User Behavior ◽  
Choice Model ◽  
Combustion Engine ◽  
Stated Preference ◽  
Operating Cost ◽  
Internal Combustion ◽  
Ann Model ◽  
Purchase Cost ◽  
Artificial Neural Network Ann

This paper presents an artificial neural network (ANN) model that simulates user’s choice of electric or internal combustion engine automotive vehicles based on basic vehicle attributes (purchase price, range, operating cost, taxes due to emissions, time to refuel/recharge and vehicle price depreciation), with the objective of analyzing user behavior and creating a model that can be used to support policymaking. The ANN was trained using stated preference data from a survey carried out in six European countries, taking into account petrol, diesel and battery electric automotive vehicle attributes. Model results show that the electric vehicle parameters (especially purchase cost, range and recharge times), as well as the purchase cost of internal combustion engine vehicles, have the most influence on consumers’ vehicle choices. A graphical interface was created for the model, to make it easier to understand the interactions between different attributes and their impacts on consumer choices and thus help policy decisions.

Modeling of lime production process using artificial neural network

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Abolghasem Daeichian ◽  
Rana Shahramfar ◽  
Elham Heidari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Production Process ◽  
High Energy ◽  
Ann Model ◽  
Artificial Neural ◽  
Rotary Kilns ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
Lime Production

Abstract Lime is a significant material in many industrial processes, including steelmaking by blast furnace. Lime production through rotary kilns is a standard method in industries, yet it has depreciation, high energy consumption, and environmental pollution. A model of the lime production process can help to not only increase our knowledge and awareness but also can help reduce its disadvantages. This paper presents a black-box model by Artificial Neural Network (ANN) for the lime production process considering pre-heater, rotary kiln, and cooler parameters. To this end, actual data are collected from Zobahan Isfahan Steel Company, Iran, which consists of 746 data obtained in a duration of one year. The proposed model considers 23 input variables, predicting the amount of produced lime as an output variable. The ANN parameters such as number of hidden layers, number of neurons in each layer, activation functions, and training algorithm are optimized. Then, the sensitivity of the optimum model to the input variables is investigated. Top-three input variables are selected on the basis of one-group sensitivity analysis and their interactions are studied. Finally, an ANN model is developed considering the top-three most effective input variables. The mean square error of the proposed models with 23 and 3 inputs are equal to 0.000693 and 0.004061, respectively, which shows a high prediction capability of the two proposed models.

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